1. Technical Field
The present invention relates generally to data storage and more particularly, but not by way of limitation, to systems and methods for storing and retrieving channel data.
2. History of Related Art
An oil well is a general term for any boring through the earth's surface that is designed to find petroleum-oil hydrocarbons. An initial life of an oil well can be viewed in three stages: planning, drilling, and completion. During these stages, a huge volume of information is generated. This information can be loosely categorized into two types: static data and real-time data. Static data is generated either in advance (e.g., modeling data, cost predictions, and well plans) or after events (e.g., daily reports, mud reports, fracture reports, and casing data). Static data can be delivered either on a regular basis (e.g. daily reports) or on a per-event basis (e.g., fracture reports and completion reports). Real-time data is sensor-derived data that is generated through either analysis of fluids (e.g., mud logs) or through deploying sensor tools in a well hole. Real-time data can be collected immediately via, for example, telemetry, or upon completion of a tool run (e.g., memory data). Real-time data can be, for example, time-indexed or depth-indexed
During the drilling and completion stages, there has been increased focus on remote support and participation. In particular, there is significant value to be obtained from an ability to analyze in real time various data streams that are generated, often through cross-comparison with data from previous wells or with models from the planning stage. A typical oil well can require collaboration from teams based at the oil well, in regional offices, and corporate headquarters. This may mean that people thousands of miles apart need to be sure they are looking at the same data sets. These data sets, in turn, may be aggregated from several different streams of data, from historical data generated in previous wells, or from models predicted during the planning stage.
Benefits can be gained from cross-correlation and analysis of this data. These benefits can include greater value in terms of accuracy of interpretation and lessons learned for future reference. Examples are comparing real-time mud and pressure readings with loss-of-time incidents reported in daily operations reports and analyzing cost information against drilling-performance indicators. Benefits can also be gained by allowing people from different disciplines and sited in different locations to work together around the same data and information so as to provide a holistic approach to data that enables better informed decisions.
Historically this has not been possible due to schisms in the organization of data and the difficulty of sharing many different types of data in the time constraints required for real-time analysis across multiple locations. Further complications have been generated by the requirement to tightly control access to this data to ensure security for what can be extremely valuable information that can have significant market impact upon an oil company.